Enterprise decision management (EDM) is an emerging technology driven by the need of businesses to automate mission-critical decisions, and to introduce precision and consistency in the decision making process. EDM employs rule based systems and analytic models for automated decision making. Businesses are adopting EDM systems to deal with increasing business decision complexity. EDM systems offer a competitive advantage to businesses operating in areas where the window of competitive advantage is short, while requirements for precision and consistency are high.
Typically, EDM systems uses historical behavioral data, prior decisions, and their outcomes to build the rule based systems and/or analytic models.
However, businesses often lack comprehensive or substantial historical data to develop accurate empirical models for critical business risk/value based decisions. Subjective data may contribute substantially to model predictive power. For most businesses, the cost, and difficulty in capturing subjective data of sufficient quality, may be too difficult. These shortcomings stem from the inherent difficulty in defining the subjective data attributes, and effectively integrating the subjective data attributes in the decision making model. Temporal stability of these data attributes also impacts model relevance and usefulness over time.
Some known EDM systems include business-specific or industry-specific packages, to reduce the amount of subjective data required for automated decision making. Although such systems may provide more accurate objective data attributes, the shortcomings related to capturing high quality data pertaining to the subjective data attributes may still remain.
Further, most known EDM systems use historical data to build the rule based systems and analytic models. Therefore, such systems may lack the ability to adapt “on-the-fly” with changing business strategies and external economic factors. In other words, known EDM systems may lack the intelligence to factor the reasoning process of human experts into the decision making models.
Therefore, what is needed is a method for creating and sustaining a model for automated decision making that is more accurate and consistent than known solutions.